针对不平地形，提出基于SE(2)的实时导航框架，提升车式机器人路径规划效率与质量。
SEB-Naver: A SE(2)-based Local Navigation Framework for Car-like Robots on Uneven Terrain
- 基于SE(2)网格的高效可通行性评估，利用GPU并行实现实时地图更新。
- 融合地形相关运动学模型的优化轨迹规划，显著提升规划效率与轨迹质量。
- 框架支持真实地形下实时感知与决策，适合复杂户外环境机器人应用。
相较于平坦地形，车式机器人在不平地形上的自主导航面临独特挑战，尤其体现在可通行性评估与关联地形的运动学建模上。本文提出SEB-Naver，一种基于SE(2)的新型局部导航框架以应对这些挑战。首先，我们设计了一种高效的SE(2)网格可通行性评估方法，利用GPU并行计算实现局部地图的实时更新与维护。其次，受微分平坦性启发，提出一种基于优化的轨迹规划方法，整合了地形相关的运动学模型，显著提升了规划效率与轨迹质量。最后，将上述组件统一集成至SEB-Naver框架中，实现了实时地形评估与轨迹优化。大量仿真与实地实验验证了该方法的有效性与高效性。代码已开源：https://github.com/ZJU-FAST-Lab/seb_naver。
原文摘要 · Abstract (English)
Autonomous navigation of car-like robots on uneven terrain poses unique challenges compared to flat terrain, particularly in traversability assessment and terrain-associated kinematic modelling for motion planning. This paper introduces SEB-Naver, a novel SE(2)-based local navigation framework designed to overcome these challenges. First, we propose an efficient traversability assessment method for SE(2) grids, leveraging GPU parallel computing to enable real-time updates and maintenance of local maps. Second, inspired by differential flatness, we present an optimization-based trajectory planning method that integrates terrain-associated kinematic models, significantly improving both planning efficiency and trajectory quality. Finally, we unify these components into SEB-Naver, achieving real-time terrain assessment and trajectory optimization. Extensive simulations and real-world experiments demonstrate the effectiveness and efficiency of our approach. The code is at https://github.com/ZJU-FAST-Lab/seb_naver.
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